Signaling across biological membranes is critical to living cells and involves membrane- embedded receptors, which transduce extracellular stimuli into cytoplasmic responses through long-range allosteric communication. G protein-coupled receptors (GPCRs) constitute the largest family among these receptors. They are encoded by more than eight hundred genes in humans and are involved in a large diversity of critical functions but also diseases, making GPCRs the target of close to 30 % of current marketed drugs. Although a wealth of genomic and functional data is available on these receptors, the lack of high-resolution structural and mechanistic information hinders the development of specific therapies to modulate their function. The goal of this proposal is to uncover the sequence, structure and energetic relationships governing GPCR signaling properties. We will address this problem using structure modeling, computational protein design, statistical analysis and experimental approaches. An immediate challenge will be to uncover the structure space sampled by representative members of naturally evolved GPCRs. [This will be achieved by modeling the conformations of inactive and active states of the receptor and identifying the networks of physical interactions mediating the allosteric transitions using structural sampling techniques that will be developed within the program RosettaMembrane.] An orthogonal question is which amino-acid sequence space encodes the folding and structural plasticity in GPCRs. It will be answered by statistical comparison between natural sequences and sequences evolved in silico under multiple physical constraints using the design mode of RosettaMembrane. As stringent tests of the accuracy of the structural, energetic and mechanistic predictions, GPCR variants with modified signaling properties will be designed and experimentally tested. The high-resolution information gained from these studies will set the stage for the rational design of GPCR variants with reprogrammed signaling properties for future structural and functional studies at the system level.